Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Orbitofrontal noradrenaline supports adaptive learning-rate adjustment in probabilistic reversal learning.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Dissecting medial temporal lobe from diencephalic sub-volumes: The amnesia dichotomy revisited.

Imaging neuroscience (Cambridge, Mass.)·2026
Same author

Self-Perceptions of Aging in Older Adults: A Network Analysis of Clinical and Non-Clinical Samples.

Brain sciences·2026
Same author

Advection, diffusion and linear transport in a single path-sampling Monte-Carlo algorithm: Getting insensitive to geometrical refinement.

PloS one·2025
Same author

Cognitive and cerebral phenotypes of neurocognitive disorders due to alcohol or Alzheimer's disease.

Brain communications·2025
Same author

Social cognition profile in early Huntington disease: Insight from neuropsychological assessment and structural neuroimaging.

Journal of Huntington's disease·2025

Related Experiment Video

Updated: Mar 25, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.6K

Sensitivity analysis enlightens effects of connectivity in a Neural Mass Model under Control-Target mode.

Anaïs Vallet1, Stéphane Blanco2, Coline Chevallier2,3

  • 1Normandie Univ, UNICAEN, PSL Research University, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, Université de Normandie, Caen, France.

Plos Computational Biology
|March 23, 2026
PubMed
Summary

This study explores how neural connections, including inhibition, impact brain function. Inhibitory connections between brain regions are crucial for implementing control, influencing model behavior and control effectiveness.

More Related Videos

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

6.1K
Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

18.6K

Related Experiment Videos

Last Updated: Mar 25, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.6K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

6.1K
Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

18.6K

Area of Science:

  • Computational neuroscience
  • Network neuroscience
  • Biophysical modeling

Background:

  • Human brain models often represent neural regions as interconnected graphs.
  • Existing models rarely consider inhibitory connectivity, limiting understanding of control mechanisms.
  • Previous work by Naskar et al. (2021) provides a foundation for neural network modeling.

Purpose of the Study:

  • To investigate how neural connectivity, specifically inhibitory connections, affects brain region behavior.
  • To extend existing biophysical brain models to include generalized connectivity patterns.
  • To analyze the emergence and effectiveness of inhibitory control within neural networks.

Main Methods:

  • Developed an extended biophysical model with excitatory and inhibitory neural pools within regions.
  • Explored four connectivity types: mutual excitation, Target inhibition by Control, Control inhibition by Target, and mutual inhibition.
  • Constructed an analytical sensitivity framework by nesting sensitivities from pool, region, to system levels.

Main Results:

  • Inhibitory control was found to emerge only in Target inhibition by Control and mutual inhibition configurations.
  • Model sensitivities were shown to depend on connectivity structure and self-inhibition strength within the Target region.
  • Connectivity structure significantly impacts control effectiveness, demonstrated via external forcing in the Control area.

Conclusions:

  • Generalized connectivity, including inhibition, is essential for implementing neural control mechanisms in brain models.
  • The nested sensitivity analysis framework provides a method for evaluating complex network configurations.
  • This approach offers a foundation for understanding how connectivity influences brain function and control.